This document discusses simulation as a research method in social science. It provides examples of different types of simulation models used in research: - System dynamics models examine complex causality and feedback in systems over time. - NK fitness landscape models study how modular systems adapt to fitness landscapes. - Genetic algorithms model evolutionary adaptation of populations to optimal forms. - Cellular automata emerge macro patterns from micro interactions of agents. - Stochastic processes incorporate probabilistic distributions into systems. The document outlines how these simulations can help answer research questions and provide insights when direct experiments are impossible or unethical.

1. Simulation Models as a
Research Method
Professor Alexander Settles

2. Research Methodology - Simulation
• Simulation as a research tool
• Research in simulation
• Focus here is on simulation of discrete
event dynamic systems

3. Social Simulation
• Most social science research uses some kind of theory or model
– Theories are generally stated in textual form
– But some are represented as equations
– Sometimes carry out experiments on artificial social systems that
would be impossible or unethical to perform on human populations
• One advantage: must think through your assumptions
– Clarity and precision; each parameter needs a value
– All the detail of the model can be inspected by others
• Disadvantage: data adequate for estimating all parameter values
may be hard to get

4. Sociology and Complexity
• The physical world is full of systems that are (almost) linear
• But (human) societies have quite unpredictable features
– Their characteristics at any one time are affected by their
past histories (‘path dependence’)
– E.g., adoption of 1 of a pair of alternative technologies by a
society can be greatly influenced by minor contingencies
about who chooses which technology early on
• Human societies, institutions and organizations are complex
systems
– The behavior of the system as a whole can’t be understood
in terms of the separate behaviors of its parts
– Contrasts with reductionist physical sciences

5. Simulation as a Research Tool
• Why simulation?
– An analytical approximation has been developed
to model some system performance measure.
– The development of the approximation requires
simplifying assumptions/approximations.
– The conjecture is that the analytical model is still a
reasonable representation of the real system.
– Simulation is being used to support or refute this
conjecture.

6. Simulation as a Research Tool
• Are the assumptions applied in the simulation
clearly stated?
– Distributions used.
– Operational protocols, e.g., blocking, etc.
– Correlation?
– Can you simulate the same system?
• Steady State vs. Terminating
– Number of runs
– Length of runs
• Some models take a long time to “settle down”

7. Simulation as a Research Tool
• Verification & validation
– Mainly applies to studying a real system or a
detailed representation
– How was this conducted?
• Results compared to an existing system?
• Comparisons made to existing analytical results?
• Extreme cases tested?

8. Simulation as a Research Tool
• Experimental design
– Experimental design?
– Random systems?
• The importance of this depends on the
way the simulation was used
– If simulating to understand a system and
gain insight, these issues become more
important

9. Methods of simulation
• System dynamics
– Behavior of a system with complex causality
and timing
– System of intersecting, circular causal loops
– Stocks that accumulate and dissipate over
time
– Flows that specify rates within system
– Inputs to a system of interconnected causal
loops, stocks, and flows produce system
outcomes

10. System Dynamics Research Tools
• Add causal loops
• Change mean of flow
rates
• Change variance of
flow rates

11. System Dynamics Research
Questions
• How do organizations undergo
fundamental change?
• When do small interruptions create major
catastrophes?
• What conditions create system instability?

12. NK fitness landscapes
• Speed and effectiveness of adaptation of
modular systems with tight versus loose
coupling to an optimal point
• System of N nodes, K coupling between
nodes
• Fitness landscape that maps performance
of all combinations

13. NK Fitness landscape
• (S, V, f) :
• S: set of admissible
solutions,
• V : S → 2S function,
:neighborhood
• S → IR: fitness
function.

14. Key Assumptions
• Adaptation via incremental moves and
long jumps
• Optimization
• Adaptation of a modular system using
search strategies (i.e., long jumps,
incremental moves) to find an optimal
point on a fitness landscape

15. NK fitness landscapes
• Vary N and K
• Change adaptation moves
• Add a “map” of the landscape
• Create an environmental jolt

16. NK fitness landscapes
• How long does it take to find an optimal
point (e.g., high-performing strategy)?
• What is the performance of the optimal
point?
• What is the optimal strategic complexity?
• How does cognition improve experiential
learning?

17. Genetic algorithms
• Adaptation of a population of agents (e.g.,
organizations) via simple learning to an
optimal agent form

18. Genetic algorithms
• Adaptation of a population of agents (e.g.,
organizations) via simple learning to an
optimal agent form
• Population of agents with genes
• Evolutionary adaptation (v-s-r)
• Variation via mutation (mistakes) and
crossover (recombination)
• Selection via fitness (performance)
• Retention via copying selected agents

19. Theoretical Logic
• Optimization
• Adaptation of a population of agents using
an evolutionary process toward an optimal
agent form

20. Research Questions
• How does adaptive learning occur within
bargaining?
• How does organizational learning affect
the evolution of a population of
organizations?
• What affects the rate of adaptation (or
learning or change)?
• When and/or does an optimal form
emerge?

21. Genetic algorithms

22. Cellular automata
• Emergence of macro
patterns from micro
interactions via spatial
processes (e.g.,
competition, diffusion)
in a population of
agents

23. Cellular automata
• Population of spatially arrayed and semi-
intelligent agents
• Agents use rules (local and global) for
interaction, some based on spatial
processes
• Neighborhood of agents where local rules
apply

24. Research Questions
• How does the pattern emerge and
change?
• How fast does a pattern emerge?
• How do competition and legitimation affect
density dependence?

25. Stochastic processes
• One or more processes by which system
operates
• One or more stochastic sources (e.g.,
process elements)
• Probablistic distributions for each
stochastic source

26. Definition
• A stochastic process is one whose
behavior is non-deterministic in that a
system's subsequent state is determined
both by the process's predictable actions
and by a random element.
• Manufacturing process
• Finance – asset pricing – Markov chain

27. Research Questions
• What is the relationship between
exploration and exploitation?
• What is the optimal degree of structure?